Non-invasive estimation of continuous arterial blood pressure using data from a wearable multi-sensor device.

Cardiovascular diseases are the leading cause of death worldwide, yet long-term continuous blood pressure monitoring, an accurate technique for prevention and early detection, remains largely unavailable outside of clinical settings. Such measurements presently require invasive, expensive, and impractical instrumentation thus preventing widespread use in non-clinical settings like at home and in research. This project aims to develop a machine learning-based algorithm that estimates continuous arterial blood pressure (cABP) from wireless wearable sensors, offering a non-invasive and accessible solution to this problem. By analyzing a unique dataset collected at Oslo University Hospital, the research will leverage multiple physiological signals, including photoplethysmography (PPG), electrocardiography (ECG), and skin temperature, to improve the accuracy and reliability of non-invasive cABP measurements in non-clinical settings. A collaboration between McGill University (Associate Prof. Georgios Mitsis, and Ph.D. candidate Rémi Dagenais) and Oslo University (Associate Prof. Ulysse Côté-Allard), this work will benefit both institutions by providing a novel method for estimating cABP non-invasively in cardiovascular and brain research while also advancing machine learning methods for multi-sensor data. Ultimately, this innovation could transform how we track blood pressure, leading to better cardiovascular health, earlier disease detection, and reduced healthcare costs.

Faculty Supervisor:

Georgios Mitsis

Student:

Partner:

University of Oslo

Discipline:

Engineering

Sector:

Biotechnology; Health and Related Sciences & Technology; Artificial Intelligence

University:

McGill University

Program: